Crack Growth During Fatigue in Ni Superalloys: Physical Origin of Stochastic Jumps and Their Predictive Role Using Statistical Approaches

镍高温合金疲劳过程中的裂纹扩展：随机跳跃的物理起源及其使用统计方法的预测作用

基本信息

批准号：
1709568
负责人：
Terence Musho
金额：
$ 42.33万
依托单位：
West Virginia University Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-06-01 至 2021-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1709568&HistoricalAwards=false
关键词：
Crack Growth During Fatigue Ni

项目摘要

Non-Technical AbstractStrong, durable materials are an integral part of our society. One such class of materials found in turbine engines, used in the aerospace and marine industries, are known as superalloys. Superalloys exhibit excellent mechanical properties (strength, creep resistance, corrosion resistance). However, there is a catch in that these materials involve a large degree of structural disorder as a result of the required material manufacturing process. The effects of such disorder become even more pronounced at the high temperatures of turbines, due to sustained loading conditions, leading to microscopic damage and cracks in the material. These cracks are exacerbated over the lifetime of the machinery. Therefore, it is crucial to understand the behavior of these cracks and prevent catastrophic mechanical failure.Crack initiation and growth in very heterogeneous materials not only can be detrimental but also very unpredictable, thus it requires statistical methods and protocols for assessing the reliability of components at various stages of fatigue loading. This project will advance the science of stochastic crack growth jumps during cyclic loading (fatigue) of metallic heterogeneous materials, with a particular focus on Ni superalloys. The usefulness of the mechanical noise produced by such little cracks is that it might contain distinctive statistical features that can identify the damage level in a turbine component. A team of engineers and scientists will combine multi-scale modeling approaches, statistical methods, and experiments to ultimately develop combined experiment and theory protocols for characterizing the fatigue-induced "cracking noise" and assessing the damage levels of mechanical components. Beyond superalloys, the very outcome of this research is to promote the progress of the fundamental understanding of fatigue damage and develop non-invasive structural prognosis methods. An educational outreach program is also planned that involves graduate, undergraduate, and high-school students, as well as the general public, in the under-represented EPSCoR state of West Virginia.Technical Abstract This project will advance the understanding of stochastic jumps during fatigue loading of Ni superalloys. A multi-scale modeling approach will be employed that will combine density functional theory (DFT) predictions with phase-field modeling. Machine-learning methods will be incorporated into the phase field model, which will be trained based on conducted experiments. The outcome of this research will be the fundamental understanding of fatigue damage that may be used to predict catastrophic failures, especially when there is limited statistical sampling.A team of engineers and scientists will develop a novel pathway to predictive modeling of crack growth during fatigue loading in metallic superalloys: By statistically sampling the noise correlations at various stages of fatigue under the assumption of constant-stress short-time tests, we will build a predictive machine-learning framework using a direct multi-step forecasting strategy. In doing so, we will investigate the fundamental origin of stochastic crack growth jumps and will develop a probabilistic model that will incorporate a first-principles relationship of the cohesive energy, generated by density functional theory predictions and phase-field modeling. To validate our models, we will conduct a series of well-controlled experiments using in-situ SEM and we will track crack growth using DC resistance drop measurements. The statistical properties of crack growth noise at various stages as a function of temperature and environmental pressure will be compared to the multi-scale model predictions. The validated multi-scale model will then be used to investigate the probability distributions of crack growth events (classified in terms of crack-length changes) during the first few cycles to predict crack growth at late stages. The outcome will be a trained model that can predict failure based on early fatigue events.This research project has a societal impact based on the fundamental physical origin of crack growth jumps during fatigue loading of metallic superalloys, which are commonly used on aircraft turbines and other hardware. The aim is to develop general protocols to promote early, safe prediction of crack growth in metallic alloys. In addition to societal impact, an educational outreach program is planned that involve training graduate, undergraduate, and high-school students, as well as the general public, in the under-represented EPSCoR state of West Virginia. The focus of training will be on the use of computational modeling materials science as well as the deep understanding of basic physical properties of crack growth, fracture, and non-equilibrium rare events. The PI will design a course that will introduce the fundamentals of non-equilibrium statistical mechanics and fracture to multidisciplinary, undergraduate engineering environments.

非技术摘要坚固耐用的材料是我们社会不可或缺的一部分。航空航天和海洋工业中使用的涡轮发动机中发现的一类材料被称为超级合金。高温合金具有优异的机械性能（强度、抗蠕变性、耐腐蚀性）。然而，有一个问题是，由于所需的材料制造过程，这些材料会出现很大程度的结构混乱。由于持续的负载条件，这种无序的影响在涡轮机的高温下变得更加明显，导致材料出现微观损伤和裂纹。这些裂纹在机器的使用寿命内会加剧。因此，了解这些裂纹的行为并防止灾难性的机械故障至关重要。非常异质材料中的裂纹萌生和扩展不仅可能是有害的，而且非常难以预测，因此需要统计方法和协议来评估部件的可靠性疲劳载荷的各个阶段。该项目将推进金属异质材料循环加载（疲劳）过程中随机裂纹扩展跳跃的科学，特别关注镍高温合金。如此小的裂纹产生的机械噪声的有用之处在于，它可能包含独特的统计特征，可以识别涡轮机部件的损坏程度。由工程师和科学家组成的团队将结合多尺度建模方法、统计方法和实验，最终开发出组合实验和理论协议，用于表征疲劳引起的“裂纹噪声”并评估机械部件的损坏水平。除了高温合金之外，这项研究的最终成果是促进了对疲劳损伤的基本认识的进步，并开发了非侵入性的结构预测方法。还计划在西弗吉尼亚州 EPSCoR 代表性不足的州开展一项教育推广计划，该计划涉及研究生、本科生、高中生以及普通公众。技术摘要该项目将增进对疲劳期间随机跳跃的理解镍高温合金的加载。将采用多尺度建模方法，将密度泛函理论（DFT）预测与相场建模相结合。机器学习方法将被纳入相场模型，该模型将根据进行的实验进行训练。这项研究的成果将是对疲劳损伤的基本理解，可用于预测灾难性故障，特别是在统计采样有限的情况下。工程师和科学家团队将开发一种新的途径来预测疲劳加载期间裂纹扩展的建模在金属高温合金中：通过在恒定应力短时测试的假设下对不同疲劳阶段的噪声相关性进行统计采样，我们将使用直接多步预测策略构建预测机器学习框架。在此过程中，我们将研究随机裂纹扩展跳跃的基本起源，并将开发一个概率模型，该模型将结合由密度泛函理论预测和相场建模生成的内聚能的第一原理关系。为了验证我们的模型，我们将使用原位 SEM 进行一系列控制良好的实验，并将使用直流电阻降测量来跟踪裂纹扩展。不同阶段裂纹扩展噪声的统计特性作为温度和环境压力的函数将与多尺度模型预测进行比较。然后，经过验证的多尺度模型将用于研究前几个周期内裂纹扩展事件（根据裂纹长度变化分类）的概率分布，以预测后期的裂纹扩展。结果将是一个训练有素的模型，可以根据早期疲劳事件预测失效。该研究项目具有社会影响，基于金属高温合金疲劳载荷期间裂纹扩展跳跃的基本物理起源，金属高温合金通常用于飞机涡轮机和其他发动机。硬件。其目的是开发通用协议，以促进金属合金裂纹扩展的早期、安全预测。除了社会影响之外，还计划开展一项教育推广计划，其中包括在 EPSCoR 代表性不足的西弗吉尼亚州对研究生、本科生和高中生以及公众进行培训。培训的重点将是使用计算建模材料科学以及对裂纹扩展、断裂和非平衡罕见事件的基本物理性质的深入理解。 PI 将设计一门课程，向多学科本科工程环境介绍非平衡统计力学和断裂的基础知识。